SoroushVahidi/scidocs huggingface dataset:
Automatic semantic classification of scientific literature according to the hallmarks of cancer
(2016)

MOTIVATION
The hallmarks of cancer have become highly influential in cancer research. They reduce the complexity of cancer into 10 principles (e.g. resisting cell death and sustaining proliferative signaling) that explain the biological capabilities acquired during the development of human tumors. Since new research depends crucially on existing knowledge, technology for semantic classification of scientific literature according to the hallmarks of cancer could greatly support literature review, knowledge discovery and applications in cancer research.


RESULTS
We present the first step toward the development of such technology. We introduce a corpus of 1499 PubMed abstracts annotated according to the scientific evidence they provide for the 10 currently known hallmarks of cancer. We use this corpus to train a system that classifies PubMed literature according to the hallmarks. The system uses supervised machine learning and rich features largely based on biomedical text mining. We report good performance in both intrinsic and extrinsic evaluations, demonstrating both the accuracy of the methodology and its potential in supporting practical cancer research. We discuss how this approach could be developed and applied further in the future.


AVAILABILITY AND IMPLEMENTATION
The corpus of hallmark-annotated PubMed abstracts and the software for classification are available at: http://www.cl.cam.ac.uk/∼sb895/HoC.html.


CONTACT
simon.baker@cl.cam.ac.uk.
Grid-connected photovoltaic systems with energy storage
(2009)

There are different interesting ways that can be followed in order to reduce costs of grid-connected photovoltaic systems, i.e., by maximizing their energy production in every operating conditions, minimizing electrical losses on the plant, utilizing grid-connected photovoltaic systems not only to generate electrical energy to be put into the power system but also to implement some other important auxiliary services normally implemented by properly conceived apparatus (electrical power quality control, load demand peak control, …), making easy the integration of different kind of renewable electrical energy resources (wind plant, fuel-cells, …).
Ontology-based personalized search and browsing
(2003)

As the number of Internet users and the number of accessible Web pages grows, it is becoming increasingly difficult for users to find documents that are relevant to their particular needs. Users must either browse through a large hierarchy of concepts to find the information for which they are looking or submit a query to a publicly available search engine and wade through hundreds of results, most of them irrelevant. The core of the problem is that whether the user is browsing or searching, whether they are an eighth grade student or a Nobel prize winner, the identical information is selected and it is presented the same way. In this paper, we report on research that adapts information navigation based on a user profile structured as a weighted concept hierarchy. A user may create his or her own concept hierarchy and use them for browsing Web sites. Or, the user profile may be created from a reference ontology by ‘watching over the user’s shoulder’ while they browse. We show that these automatically created profiles reflect the user’s interests quite well and they are able to produce moderate improvements when applied to search results.
Learning-to-Rank in Research Paper CBF recommendation: Leveraging Irrelevant Papers
(2016)

Suggesting relevant literature to researchers has become an active area of study, typically relying on content-based filtering (CBF) over the rich textual features available. Given the high dimensionality and the sparsity of the training samples inherent to this domain, the focus has so far been on heuristic-based methods. In this paper, we argue for the model-based approach and propose a learning-to-rank method that leverages publicly available publications’ metadata to produce an effective prediction model. The proposed method is systematically evaluated on a scholarly paper recommendation dataset and compared against state-of-the-art model-based approaches as well as current, domain-specific heuristic methods. The results show that our approach clearly outperforms state-of-the-art research paper recommendations utilizing only publicly available meta-data.
Upper lateral cartilage fold-in flap: a combined spreader and/or splay graft effect without cartilage grafts.
(2008)

Dorsal hump reduction almost always breaks the internal nasal valve and nasal obstruction is likely to occur postoperatively, unless reconstructed. One hundred eighty patients were operated using both open and closed rhinoplasty approaches. Upper lateral cartilages were meticulously separated from their junction with septum. Following bony and septal cartilaginous hump removal, upper lateral cartilages were folded inward. Either transcartilaginous horizontal mattress/simple sutures or perichondrial sutures were used depending of the desired width of the middle vault and the necessity for a splay-graft effect. In 7 patients unilateral, and in 1 patient bilateral, nasal synechia occurred and they were all treated under local anesthesia. All patients but 9 stated significantly improved nasal breathing. There was no inverted-V deformity or middle-vault narrowing observed. This technique is simple and physiologic, might be applicable for almost all primary rhinoplasty patients. Although it is possible with closed rhinoplasty approaches, it is easier with an open approach.
Light warping for enhanced surface depiction
(2009)

Recent research on the human visual system shows that our perception of object shape relies in part on compression and stretching of the reflected lighting environment onto its surface. We use this property to enhance the shape depiction of 3D objects by locally warping the environment lighting around main surface features. Contrary to previous work, which require specific illumination, material characteristics and/or stylization choices, our approach enhances surface shape without impairing the desired appearance.
 Thanks to our novel local shape descriptor, salient surface features are explicitly extracted in a view-dependent fashion at various scales without the need of any pre-process. We demonstrate our system on a variety of rendering settings, using object materials ranging from diffuse to glossy, to mirror or refractive, with direct or global illumination, and providing styles that range from photorealistic to non-photorealistic. The warping itself is very fast to compute on modern graphics hardware, enabling real-time performance in direct illumination scenarios.
 Note: Third-Party Material Attribution  Third-party material used in ACM Transactions on Graphics 28(3), Article 25 - "Light Warping for Enhanced Surface Depiction," by Vergne, Pacanowski, Barla, Granier, and Schlick - was used without proper attribution.
 The 3D model used in Figures 1, 3, and 5, as well as in the cover image of this volume of the journal, was downloaded from the Shape Repository of AIM@SHAPE Project (http://shapes.aimatshape.net) and is the property of CNR-IMATI.
 We regret this oversight.
Fully Distributed Scrum: The Secret Sauce for Hyperproductive Offshored Development Teams
(2008)

Scrum was designed to achieve a hyperproductive state where productivity increases 5-10 times over industry averages and many collocated teams have achieved this effect. The question for this paper is whether distributed, offshore teams can consistently achieve the hyperproductive state. In particular, can a team establish a localized velocity and then maintain or increase that velocity when distributing teams across continents. Since 2006, Xebia started projects with half Dutch and half Indian team members. After establishing localized hyperproductivity, they move the Indian members of the team to India and show increasing velocity with fully distributed teams. After running XP engineering practices inside many distributed Scrum projects, Xebia has systematically productized a model very similar to the SirsiDynix model (J. Sutherland, 2006) for high performance, distributed, offshore teams with outstanding quality.
A* CCG Parsing with a Supertag-factored Model
(2014)

We introduce a new CCG parsing model which is factored on lexical category assignments. Parsing is then simply a deterministic search for the most probable category sequence that supports a CCG derivation. The parser is extremely simple, with a tiny feature set, no POS tagger, and no statistical model of the derivation or dependencies. Formulating the model in this way allows a highly effective heuristic for A∗ parsing, which makes parsing extremely fast. Compared to the standard C&C CCG parser, our model is more accurate out-of-domain, is four times faster, has higher coverage, and is greatly simplified. We also show that using our parser improves the performance of a state-ofthe-art question answering system.
Joint A* CCG Parsing and Semantic Role Labelling
(2015)

Joint models of syntactic and semantic parsing have the potential to improve performance on both tasks—but to date, the best results have been achieved with pipelines. We introduce a joint model using CCG, which is motivated by the close link between CCG syntax and semantics. Semantic roles are recovered by labelling the deep dependency structures produced by the grammar. Furthermore, because CCG is lexicalized, we show it is possible to factor the parsing model over words and introduce a new A∗ parsing algorithm— which we demonstrate is faster and more accurate than adaptive supertagging. Our joint model is the first to substantially improve both syntactic and semantic accuracy over a comparable pipeline, and also achieves state-of-the-art results for a nonensemble semantic role labelling model.
LSTM CCG Parsing
(2016)

We demonstrate that a state-of-the-art parser can be built using only a lexical tagging model and a deterministic grammar, with no explicit model of bi-lexical dependencies. Instead, all dependencies are implicitly encoded in an LSTM supertagger that assigns CCG lexical categories. The parser significantly outperforms all previously published CCG results, supports efficient and optimal A∗ decoding, and benefits substantially from semisupervised tri-training. We give a detailed analysis, demonstrating that the parser can recover long-range dependencies with high accuracy and that the semi-supervised learning enables significant accuracy gains. By running the LSTM on a GPU, we are able to parse over 2600 sentences per second while improving state-of-the-art accuracy by 1.1 F1 in domain and up to 4.5 F1 out of domain.
Chainer : a Next-Generation Open Source Framework for Deep Learning
(2015)

Software frameworks for neural networks play key roles in the development and application of deep learning methods. However, as new types of deep learning models are developed, existing frameworks designed for convolutional neural networks are becoming less useful. In this paper, we introduce Chainer, a Pythonbased, standalone open source framework for deep learning models. Chainer provides a flexible, intuitive, and high performance means of implementing a full range of deep learning models, including state-of-the-art models such as recurrent neural networks and variational autoencoders.
Multi-level Lasso for Sparse Multi-task Regression
(2012)

We present a flexible formulation for variable selection in multi-task regression to allow for discrepancies in the estimated sparsity patterns accross the multiple tasks, while leveraging the common structure among them. Our approach is based on an intuitive decomposition of the regression coefficients into a product between a component that is common to all tasks and another component that captures task-specificity. This decomposition yields the Multi-level Lasso objective that can be solved efficiently via alternating optimization. The analysis of the “orthonormal design” case reveals some interesting insights on the nature of the shrinkage performed by our method, compared to that of related work. Theoretical guarantees are provided on the consistency of Multi-level Lasso. Simulations and empirical study of micro-array data further demonstrate the value of our framework.
How Much Is Enough? Choosing ε for Differential Privacy
(2011)

Differential privacy is a recent notion, and while it is nice conceptually it has been difficult to apply in practice. The parameters of differential privacy have an intuitive theoretical interpretation, but the implications and impacts on the risk of disclosure in practice have not yet been studied, and choosing appropriate values for them is non-trivial. Although the privacy parameter in differential privacy is used to quantify the privacy risk posed by releasing statistics computed on sensitive data, is not an absolute measure of privacy but rather a relative measure. In effect, even for the same value of , the privacy guarantees enforced by differential privacy are different based on the domain of attribute in question and the query supported. We consider the probability of identifying any particular individual as being in the database, and demonstrate the challenge of setting the proper value of given the goal of protecting individuals in the database with some fixed probability.
Coupling Distributed and Symbolic Execution for Natural Language Queries
(2017)

Building neural networks to query a knowledge base (a table) with natural language is an emerging research topic in deep learning. An executor for table querying typically requires multiple steps of execution because queries may have complicated structures. In previous studies, researchers have developed either fully distributed executors or symbolic executors for table querying. A distributed executor can be trained in an end-to-end fashion, but is weak in terms of execution efficiency and explicit interpretability. A symbolic executor is efficient in execution, but is very difficult to train especially at initial stages. In this paper, we propose to couple distributed and symbolic execution for natural language queries, where the symbolic executor is pretrained with the distributed executor’s intermediate execution results in a step-by-step fashion. Experiments show that our approach significantly outperforms both distributed and symbolic executors, exhibiting high accuracy, high learning efficiency, high execution efficiency, and high interpretability.
A High-Speed and Ultra Low-Power Subthreshold Signal Level Shifter
(2017)

In this paper, we present a novel level shifter circuit converting subthreshold signal levels to super-threshold signal levels at high-speed using ultra low-power and a small silicon area, making it well-suited for low-power applications such as wireless sensor networks and implantable medical devices. The proposed circuit introduces a new voltage level shifter topology employing a level-shifting capacitor contributing to increase the range of conversion voltages, while significantly reducing the conversion delay. Such a level-shifting capacitor is quickly charged, whenever the input signal detects a low-to-high transition, in order to boost internal voltage nodes, and quickly reach a high output voltage level. The proposed circuit achieves a shorter propagation delay and a smaller silicon area for a given operating frequency and power consumption compared to other circuit solutions. Measurement results are presented for the proposed circuit fabricated in a 0.18- $\mu \text {m}$  TSMC technology. The proposed circuit can convert a wide range of the input voltages from 330 mV to 1.8 V, and operate over a frequency range of 100 Hz to 100 MHz. It has a propagation delay of 29 ns and a power consumption of 61.5 nW for input signals 0.4 V, at a frequency of 500-kHz, outperforming previous designs.
Developing Non-goal Dialog System Based on Examples of Drama Television
(2014)

This paper presents a design and experiments of developing a non-goal dialog system by utilizing human-to-human conversation examples from drama television. The aim is to build a conversational agent that can interact with users in as natural a fashion as possible, while reducing the time requirement for database design and collection. A number of the challenging design issues we faced are described, including (1) filtering and constructing a dialog example database from the drama conversations, and (2) retrieving a proper system response by finding the best dialog example based on the current user query. Subjective evaluation from a small user study is also discussed.
Asynchronous frameless event-based optical flow
(2012)

This paper introduces a process to compute optical flow using an asynchronous event-based retina at high speed and low computational load. A new generation of artificial vision sensors has now started to rely on biologically inspired designs for light acquisition. Biological retinas, and their artificial counterparts, are totally asynchronous and data driven and rely on a paradigm of light acquisition radically different from most of the currently used frame-grabber technologies. This paper introduces a framework for processing visual data using asynchronous event-based acquisition, providing a method for the evaluation of optical flow. The paper shows that current limitations of optical flow computation can be overcome by using event-based visual acquisition, where high data sparseness and high temporal resolution permit the computation of optical flow with micro-second accuracy and at very low computational cost.
Graphene plasmonics for terahertz to mid-infrared applications.
(2014)

In recent years, we have seen a rapid progress in the field of graphene plasmonics, motivated by graphene's unique electrical and optical properties, tunability, long-lived collective excitation and its extreme light confinement. Here, we review the basic properties of graphene plasmons: their energy dispersion, localization and propagation, plasmon-phonon hybridization, lifetimes and damping pathways. The application space of graphene plasmonics lies in the technologically significant, but relatively unexploited terahertz to mid-infrared regime. We discuss emerging and potential applications, such as modulators, notch filters, polarizers, mid-infrared photodetectors, and mid-infrared vibrational spectroscopy, among many others.
Learning a Probabilistic Topology Discovering Model for Scene Categorization
(2015)

A recent advance in scene categorization prefers a topological based modeling to capture the existence and relationships among different scene components. To that effect, local features are typically used to handle photographing variances such as occlusions and clutters. However, in many cases, the local features alone cannot well capture the scene semantics since they are extracted from tiny regions (e.g., 4 × 4 patches) within an image. In this paper, we mine a discriminative topology and a low-redundant topology from the local descriptors under a probabilistic perspective, which are further integrated into a boosting framework for scene categorization. In particular, by decomposing a scene image into basic components, a graphlet model is used to describe their spatial interactions. Accordingly, scene categorization is formulated as an intergraphlet matching problem. The above procedure is further accelerated by introducing a probabilistic based representative topology selection scheme that makes the pairwise graphlet comparison trackable despite their exponentially increasing volumes. The selected graphlets are highly discriminative and independent, characterizing the topological characteristics of scene images. A weak learner is subsequently trained for each topology, which are boosted together to jointly describe the scene image. In our experiment, the visualized graphlets demonstrate that the mined topological patterns are representative to scene categories, and our proposed method beats state-of-the-art models on five popular scene data sets.
An open-label study to evaluate sildenafil for the treatment of lymphatic malformations.
(2014)

BACKGROUND
Lymphatic malformations can be challenging to treat. Mainstay interventions including surgery and sclerotherapy are invasive and can result in local recurrence and complications.


OBJECTIVE
We sought to assess the effect of 20 weeks of oral sildenafil on reducing lymphatic malformation volume and symptoms in children.


METHODS
Seven children (4 boys, 3 girls; ages 13-85 months) with lymphatic malformations were given oral sildenafil for 20 weeks in this open-label study. The volume of the lymphatic malformation was calculated blindly using magnetic resonance imaging performed before and after 20 weeks of sildenafil. Lymphatic malformations were assessed clinically on weeks 4, 12, 20, and 32. Both the physician and parents evaluated the lymphatic malformation in comparison with baseline.


RESULTS
Four subjects had a lymphatic malformation volume decrease (1.0%-31.7%). In 2 subjects, despite a lymphatic malformation volume increase (1.1%-3.7%), clinical improvement was noted while on sildenafil. One subject had a 29.6% increase in lymphatic malformation volume and no therapeutic response. Lymphatic malformations of all 6 subjects who experienced a therapeutic response on sildenafil softened and became easily compressible. Adverse events were minimal.


LIMITATIONS
A randomized controlled trial will be necessary to verify the effects of sildenafil on lymphatic malformations.


CONCLUSIONS
Sildenafil can reduce lymphatic malformation volume and symptoms in some children.
Data mining a diabetic data warehouse
(2002)

Diabetes is a major health problem in the United States. There is a long history of diabetic registries and databases with systematically collected patient information. We examine one such diabetic data warehouse, showing a method of applying data mining techniques, and some of the data issues, analysis problems, and results. The diabetic data warehouse is from a large integrated health care system in the New Orleans area with 30,383 diabetic patients. Methods for translating a complex relational database with time series and sequencing information to a flat file suitable for data mining are challenging. We discuss two variables in detail, a comorbidity index and the HgbA1c, a measure of glycemic control related to outcomes. We used the classification tree approach in Classification and Regression Trees (CART) with a binary target variable of HgbA1c >9.5 and 10 predictors: age, sex, emergency department visits, office visits, comorbidity index, dyslipidemia, hypertension, cardiovascular disease, retinopathy, end-stage renal disease. Unexpectedly, the most important variable associated with bad glycemic control is younger age, not the comorbiditity index or whether patients have related diseases. If we want to target diabetics with bad HgbA1c values, the odds of finding them is 3.2 times as high in those <65 years of age than those older. Data mining can discover novel associations that are useful to clinicians and administrators [corrected].